TEAM: Yu Liu, Ximing Zhong, Que Guan, Jiaxin Tao
Mechanical AI can perform hundreds of different scenario designs. Human design perception can be wrapped in a machine. Design practice can become modular and accessible through tools. The line between instrumental and architectural knowledge is becoming increasingly blurred. Tool-encoded knowledge actually represents a new collective cultural memory.
—— MEMEX 1945
Conceptual Introduction and Theoretical Framework
With the change and deterioration of the global climate and environment, more and more people are concerned about the current human condition, and constantly proposing various sustainable response strategies to solve existing problems and future situations, to seek a balanced and optimized solution strategy. From the point of view of artificial intelligence, we attempt to explore experiments in which artificial intelligence imitates, learns, optimizes, and rapidly generates solutions. Through testing and predicting the climatic environment of the site and learning from high-quality examples, we are able to set different environmental and climatic conditions and needs and quickly generate sustainable solutions for different geographical environments, site characteristics, and climatic types. We seek a relationship between architecture and landscape, as well as future extensible states.
During the cloning process, whether the Dolly sheep of architectural design could preserve human intelligence? Could we integrate a cloning and adaptive framework to migrate the order of human decision-making to meet new design solutions and new contexts, where there is decision-making, there is the hustle and bustle, and how could we avoid it? Who is more suitable for cloning the decision-making part of human intelligence, machine learning, or probabilistic mathematical methods, we try to explore the deep mathematical and probabilistic logic behind the form, and for machine learning, what theoretical foundations we need to generate valid parts. Through the massive and rapid self-learning, solution optimization, and complex calculations of artificial intelligence, it is hoped that more effective outcome prediction and faster and more rational sustainable solutions will be made in urban new and renovation environments.
Material Dimensions and Construction Costs
The whole installation is divided into a central tower and standard units around it, with a height of 2.5m and a length and width of 1m. The materials used include steel/wood structure, density board, painted aluminum tubes, wire, light strips, spotlights, iron display stands, etc. The overall cost estimate (materials + labor) is around RMB 50,000.
Artificial intelligence defines the semantics by learning landscape cases under different climatic conditions and generates the learned semantics appropriately in the new site context through the given site context.
We visualized the design and thinking logic of the AI in a 4m x 4m site, cloning and evolving the abstract ai to communicate it physically in the installation, which is divided into a central tower and surrounding standard units, with the surrounding units being cases sampled by the AI, which are filtered by wind and sunlight simulations. The AI learns from the surrounding landscape design solutions and integrates them into the tower as input. These input scenarios are integrated into a single reference scenario. The AI processing unit from bottom to top is shown learning cases, design language definition, design element migration evolution, design element reorganization, and new solution generation. From top to bottom the nodes and the complex computational processes of AI machine thinking are shown. The different colors in the installation represent different design semantics. This is expressed in different colors by way of copper pipes and layered so that the form presented in the lower half of the tower can be seen, while in the center of the tower the AI performs intelligent solution generation based on the layout and needs of the new site. The spatial semantics that the AI learns are migrated and evolved in the new site, so here we connect and position the same spatial semantics with coloured copper wire, and a new spatial result is generated, with the final new solution presented at the top.